Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bezier curve-based model for path planning. The control points of the Bezier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bezier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

Purpose




The purpose of this paper is to consider the smooth path planning problem for a mobile robot based on the genetic algorithm (GA) and the Bezier curve.




Design/methodology/approach




The workspace of a mobile robot is described by a new grid-based representation that facilitates the operations of the adopted GA. The chromosome of the GA is composed of a sequence of binary numbered grids (i.e. control points of the Bezier curve). Ordinary genetic operators including crossover and mutation are used to search the optimum chromosome where the optimization criterion is the length of a piecewise collision-free Bezier curve path determined by the control points.




Findings




This paper has proposed a new smooth path planning for a mobile robot by resorting to the GA and the Bezier curve. A new grid-based representation of the workspace has been presented, which makes it convenient to perform operations in the GA. The GA has been used to search the optimum control points that determine the Bezier curve-based smooth path. The effectiveness of the proposed approach has been verified by a numerical experiment, and some performances of the obtained method have also been analyzed.




Research limitations/implications




There still remain many interesting topics, for example, how to solve the specific smooth path planning problem by using the GA and how to promote the computational efficiency in the more grids case. These issues deserve further research.




Originality/value




The purpose of this paper is to improve the existing results by making the following three distinctive contributions: a rigorous mathematical formulation of the path planning optimization problem is formulated; a general grid-based representation (2n × 2n) is proposed to describe the workspace of the mobile robots to facilitate the implementation of the GA where n is chosen according to the trade-off between the accuracy and the computational burden; and the control points of the Bezier curve are directly linked to the optimization criteria so that the generated paths are guaranteed to be optimal without any need for smoothing afterwards.

/pdf/a-new-genetic-algorithm-approach-to-smooth-path-planning-for-jllgeznyrg.pdf

A new genetic algorithm approach to smooth path planning for mobile robots

Purpose




This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment.




Design/methodology/approach




The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method.




Findings




Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method.




Originality/value




Therefore, this can provide a new method for the area of path planning of intelligent robot.

Path planning for intelligent robot based on switching local evolutionary PSO algorithm

Energy efficiency of manufacturing systems: A review of energy assessment methods and tools

Purpose – The purpose of this paper is to investigate how to make tele‐operated tasks easier using an expert system to interpret joystick and sensor data.Design/methodology/approach – Current tele‐operated systems tend to rely heavily on visual feedback and experienced operators. Simple expert systems improve the interaction between an operator and a tele‐operated mobile‐robot using ultrasonic sensors. Systems identify potentially hazardous situations and recommend safe courses of action. Because pairs of tests and results took place, it was possible to use a paired‐samples statistical test.Findings – Results are presented from a series of timed tasks completed by tele‐operators using a joystick to control a mobile‐robot via an umbilical cable. Tele‐operators completed tests both with and without sensors and with and without the new expert system and using a recently published system to compare results. The t‐test was used to compare the means of the samples in the results.Research limitations/implication...

/pdf/simple-expert-systems-to-improve-an-ultrasonic-sensor-system-3nss0fm7ob.pdf

Simple expert systems to improve an ultrasonic sensor‐system for a tele‐operated mobile‐robot

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- ...

/pdf/ambient-intelligence-for-optimal-manufacturing-and-energy-42x6qyt727.pdf

Ambient intelligence for optimal manufacturing and energy efficiency

The effect on failure rates of the way tele-operators interact with mobile robots is investigated. Human tele-operators attempted to move a robot through progressively more complicated environments with reducing gaps, as quickly as possible. Tele-operators used a joystick and either watched robots, while operating them, or used a computer screen to view scenes remotely. Cameras were either mounted on the robot to view the space ahead of the robot or mounted remotely so that they viewed both the environment and robot. Tele-operators completed tests both with and without sensors. Both an umbilical cable and a radio link were used.

Analysis of successes and failures with a tele-operated mobile robot in various modes of operation

Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combine real time ambient sensing with Artificial Intelligence and Knowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient data will provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems.

/pdf/making-decisions-about-saving-energy-in-compressed-air-3ldl9lhq96.pdf

Making decisions about saving energy in compressed air systems using Ambient Intelligence and Artificial Intelligence

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/opti...

David Robinson

Papers

Simple expert systems to improve an ultrasonic sensor‐system for a tele‐operated mobile‐robot

Ambient intelligence for optimal manufacturing and energy efficiency

Analysis of successes and failures with a tele-operated mobile robot in various modes of operation

Making decisions about saving energy in compressed air systems using Ambient Intelligence and Artificial Intelligence

Sensor-based ambient intelligence for optimal energy efficiency